Food Image Segmentation

Food image segmentation aims to automatically identify and delineate different food items within images and videos, facilitating applications in nutrition analysis, food quality control, and agricultural technology. Current research focuses on developing robust and efficient segmentation models, employing architectures like transformers and convolutional neural networks, often enhanced by techniques such as memory-based tracking for video processing and open-vocabulary approaches to handle diverse food items. These advancements improve the accuracy and speed of food segmentation, leading to more precise dietary assessments, optimized food production processes, and enhanced automation in various food-related industries.